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Ecological Effects of Nitrogen Deposition in the Western United States

2003· article· en· W2165551548 on OpenAlexfundno aff
Mark E. Fenn, Jill S. Baron, Edith B. Allen, Heather M. Rueth, Koren R. Nydick, Linda H. Geiser, William D. Bowman, James O. Sickman, T. Meixner, Dale W. Johnson, Peter Neitlich

Bibliographic record

VenueBioScience · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsnot available
FundersUniversity of California, RiversideBritish Columbia Institute of Technology
KeywordsChaparralDeposition (geology)Environmental scienceThreatened speciesEcologyNitrateLichenTerrestrial ecosystemHabitatEcosystemPhytoplanktonNutrientBiologySediment

Abstract

fetched live from OpenAlex

Abstract In the western United States vast acreages of land are exposed to low levels of atmospheric nitrogen (N) deposition, with interspersed hotspots of elevated N deposition downwind of large, expanding metropolitan centers or large agricultural operations. Biological response studies in western North America demonstrate that some aquatic and terrestrial plant and microbial communities are significantly altered by N deposition. Greater plant productivity is counterbalanced by biotic community changes and deleterious effects on sensitive organisms (lichens and phytoplankton) that respond to low inputs of N (3 to 8 kilograms N per hectare per year). Streamwater nitrate concentrations are elevated in high-elevation catchments in Colorado and are unusually high in southern California and in some chaparral catchments in the southwestern Sierra Nevada. Chronic N deposition in the West is implicated in increased fire frequency in some areas and habitat alteration for threatened species. Between hotspots, N deposition is too low to cause noticeable effects or has not been studied.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score0.435

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.205
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations655
Published2003
Admission routes1
Has abstractyes

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